Conventional computers (e.g., von Neuman computers) are built according to the universal computing concept of Alan Turing, using serial instructions, featuring macro-level separation of the memory and the processing. As Moore's law is approaching a limit, Complementary metal-oxide-semiconductor (CMOS)-based Boolean logic, which can consume megawatts of power and take hours to carry out complex, nonlinear, and non-sequential calculations, is not as efficient as real time biological neural information processing systems, which consume in the order of tens of watts of power.
A new computing paradigm using artificial neural networks is being developed based on the computing archetype of the brains, to resolve various disadvantages associated with conventional von Neuman computing, which include, e.g., power inefficiency, serial executions, and synchronous and programming intensive issues. In this new computing paradigm, a complete harness of biologically inspired concepts like activation thresholds and weighted connections relies on hardware implementation of spiking neurons and their dynamically involving synapses. Such hardware approaches promise inherent low power and ultra-large-scale integration, which is challenging to achieve in software simulation on conventional computers.